
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "15"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 101 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 97 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.690806	-0.245293	3.817408	4.135873	0.216091	0.194628	3.130903	3.799079	0.7385	0.7234	0.2454	12.531852	11.180727
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.423086	1.033917	8.923454	9.371509	1.907010	6.628486	3.384465	4.008574	0.6505	0.6318	0.2079	7.861748	6.951053
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.786883	-1.086396	-0.426436	-0.659895	0.568145	-0.395882	2.093523	0.043483	0.0296	0.0279	0.0012	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.071596	0.376277	6.409917	5.989932	0.343218	1.585976	1.110224	0.435987	0.5306	0.5390	0.3076	7.892217	7.211474
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.583313	1.272084	10.159344	9.710131	0.613416	4.072893	1.108276	1.212433	0.5341	0.5393	0.3002	7.225692	6.511936
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.576603	0.923954	5.377456	5.405728	1.641353	1.764570	2.565708	2.410723	0.6242	0.6040	0.2307	6.680944	6.035748
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.587042	0.155364	5.440607	6.485678	2.166567	1.464511	1.938461	1.960046	0.7167	0.6814	0.2036	13.888737	10.172551
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	1.874629	0.907553	15.964324	17.099786	0.378669	0.668790	3.089201	0.850294	nan	nan	nan	12.034603	14.367401
2459586	digital_ok	0.00%	0.00%	0.54%	0.00%	3.78%	1.08%	0.373010	-0.508084	0.911533	-0.497124	0.072365	-1.155153	0.748623	0.377046	0.5413	0.5479	0.2906	1.867030	1.723856
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.207184	0.647364	2.380383	1.456336	2.245869	0.800694	3.372806	1.109235	0.0309	0.0311	0.0013	0.000000	0.000000
2459584	digital_ok	0.00%	33.01%	36.61%	0.00%	37.72%	0.00%	0.979665	-0.037357	-0.502719	-0.596548	-0.825835	-0.495367	-0.645443	1.427416	0.4819	0.4871	0.3009	1.573254	1.510330
2459583	digital_ok	0.00%	0.54%	0.54%	0.00%	5.91%	0.54%	0.381082	-0.199840	2.266029	2.181532	-0.399378	0.012455	-0.204541	1.830082	0.5220	0.5244	0.3247	1.640099	1.761710
2459582	digital_ok	0.00%	2.70%	6.49%	0.00%	9.73%	0.00%	-0.022623	-0.168814	0.329281	0.524072	-0.939427	0.148572	0.493389	0.746800	0.5243	0.5272	0.3215	1.759485	1.622548
2459581	digital_ok	0.00%	0.00%	1.08%	0.00%	4.32%	0.00%	0.214536	0.057954	0.925534	0.043671	-0.591556	0.370874	0.234819	1.697644	0.5375	0.5394	0.3206	1.792756	1.661848
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.016833	0.203509	2.254463	1.791936	-0.318515	0.750540	-0.214826	0.185608	0.5423	0.5489	0.3178	1.751921	1.738541
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.272801	0.013053	0.734659	0.821881	-0.660748	-0.319127	0.243466	1.381333	0.5381	0.5459	0.3202	1.925793	1.846769
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.164778	-0.543702	-0.917595	-0.266961	-1.004752	0.060661	-1.020587	0.681960	0.0296	0.0284	0.0009	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.359259	-0.100534	0.716315	0.124279	-0.378463	1.167786	-0.310048	1.076171	0.5437	0.5505	0.3155	1.842068	1.755954
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.193306	0.140606	0.252501	0.289244	-0.986852	-0.793425	0.384503	0.265216	0.0496	0.0500	0.0036	0.966205	0.959659
2459574	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.631927	-0.135354	0.449047	0.155707	0.435968	1.196940	2.117465	0.643806	0.0729	0.0694	0.0074	1.199024	1.203136
2459573	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.279792	0.156132	0.710331	0.483283	-0.460062	0.258587	0.126369	1.586113	0.0508	0.0585	0.0032	0.000000	0.000000
2459572	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.678687	0.424829	1.717795	1.544263	0.024221	0.720970	0.739571	2.247373	0.0763	0.0669	0.0130	1.102453	1.105629
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.006396	-0.518829	-0.225438	-0.724098	-0.909237	0.561908	-0.915486	0.911688	0.0293	0.0304	0.0008	nan	nan
2459570	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.186687	-0.387916	0.331631	-0.688840	0.793903	0.199637	0.443964	0.422931	0.0392	0.0418	0.0027	0.000000	0.000000
2459569	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.985358	0.307375	0.651915	0.389124	0.408461	0.424190	0.428264	0.294150	0.0707	0.0767	0.0055	1.206193	1.204296
2459566	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.742107	-0.271054	0.806539	0.264524	-0.649806	0.211964	-0.354311	1.269313	0.0743	0.0692	0.0077	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	73.33%	0.00%	0.070714	0.070714	0.026519	0.026519	0.526814	0.526814	0.051858	0.051858	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-1.047780	-0.004816	-0.482579	0.703505	2.003350	0.251389	-0.019068	1.774448	0.7031	0.6979	0.4024	2.084812	1.769291
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.454509	0.413938	1.515125	1.133894	0.341600	1.270785	0.450895	0.279487	0.6957	0.6959	0.3884	1.859074	1.700495
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.98%	0.00%	-0.589795	-0.070046	2.033615	2.235842	-0.600140	0.891841	0.056114	0.708540	0.6965	0.6934	0.3985	1.657678	1.615280
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0303	0.0307	0.0010	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.320637	0.329308	2.314129	2.486393	0.001020	0.866659	0.566610	2.260751	0.6991	0.6958	0.3947	1.717654	1.704271
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6893	0.6784	0.4179	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6916	0.6657	0.3977	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.134195	4.896480	3.573393	2.484782	0.202532	-0.624580	0.144115	0.498375	0.7081	0.6879	0.4000	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.887124	1.939226	0.668966	0.458343	0.526204	0.371940	0.309993	1.018409	0.7000	0.6841	0.3949	1.977424	1.765557
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0469	0.0466	0.0009	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.811140	3.174605	0.707854	0.350994	-0.608280	-0.073755	-0.290374	2.951782	0.7010	0.6850	0.4060	1.846867	1.800106
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-0.401050	3.945466	-0.591543	1.048611	0.358247	-0.489396	-0.572511	0.922874	0.7603	0.7373	0.3624	2.009192	1.666582
2459541	digital_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	0.207022	4.388109	2.824237	2.557195	5.153120	2.823895	2.478380	2.704525	0.7505	0.7247	0.3347	6.575389	4.964314
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-0.962054	0.204317	0.290431	0.430288	0.394882	-0.251055	-0.251530	0.418834	0.7539	0.7387	0.3645	1.515835	1.433626
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	-0.579089	1.456962	2.225902	1.046346	0.796653	2.931195	-0.397260	1.908028	0.7285	0.7299	0.4262	1.896764	1.760350
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.804983	0.197375	0.429225	0.428165	0.616098	0.425351	-0.309168	0.121685	0.7790	0.7563	0.3755	1.817068	1.628885
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.45%	-0.814616	0.460057	1.118112	1.117040	1.657409	1.285890	-0.110922	0.176366	0.7735	0.7557	0.3679	2.033963	1.835834
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.119356	1.126299	2.076338	2.142065	-0.172831	0.621433	4.608791	2.605523	0.7115	0.7100	0.4143	4.343708	3.767089
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.034082	0.851280	0.813780	1.083247	-0.170970	0.207169	4.294167	2.439178	0.7110	0.7058	0.4153	4.724082	4.099395
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.408963	0.582814	1.205228	1.102810	0.519324	1.242327	1.860794	1.105443	0.7301	0.7233	0.4124	1.894406	1.722883
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.685682	0.659392	0.622652	0.336348	0.660791	-0.511279	0.216035	0.308040	0.8959	0.9266	0.3251	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.720294	0.877663	1.587869	0.518316	1.267576	1.040566	6.385283	2.623718	nan	nan	nan	6.261446	5.088987
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.508774	1.626074	3.558653	3.012944	0.177865	0.037943	6.715850	2.773938	0.6954	0.6402	0.3709	5.883848	7.330440
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.865453	-0.485570	-1.120908	-0.855590	-1.202505	-0.869313	-1.135678	0.248365	0.0319	0.0302	0.0010	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.695740	0.798301	0.055936	0.571658	-0.923268	-0.754925	-0.404912	0.542000	0.9003	0.9317	0.2044	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.053421	1.054404	3.098596	3.253431	-0.644732	0.411978	2.280734	1.708550	0.8884	0.8583	0.2036	8.547521	5.750837
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.423678	0.933863	1.024718	0.888312	1.062261	-0.167917	0.784215	1.644706	0.8460	0.8405	0.4856	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.484829	0.725808	-0.203937	0.121686	-0.158337	-0.342763	1.577944	3.344687	0.1178	0.0890	0.0117	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.007618	0.360287	-0.621675	-0.338227	-0.278465	0.308039	-0.286178	0.201602	0.1175	0.0812	0.0109	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.717219	0.759079	-0.072864	0.438700	0.072100	0.649442	-0.205538	-0.016759	0.1011	0.0975	0.0088	0.000000	0.000000
2459498	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.601060	0.494849	0.104150	-0.057296	0.395942	0.051787	5.936062	0.387643	0.1223	0.0962	0.0158	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.532953	0.570117	-0.120758	-0.006948	0.229461	1.136804	2.808351	0.253926	0.1132	0.1173	0.0205	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.671189	0.542770	0.367535	0.028626	0.628566	0.671089	1.766208	-0.320893	0.1459	0.1000	0.0157	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.331028	0.542393	0.295350	0.031093	0.549751	0.700607	0.125746	0.070156	0.1303	0.1180	0.0185	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.575805	0.523236	-0.063052	-0.139875	0.283328	0.462439	0.469425	-0.210257	0.1445	0.1057	0.0182	0.000000	0.000000
2459492	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.523014	2.286805	3.042828	2.761143	1.547364	0.090532	7.353971	5.463257	0.7434	0.7983	0.3571	5.331825	5.358657
2459491	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.236612	2.756205	3.810261	3.443764	2.382269	2.047799	7.694452	3.312973	0.7886	0.8171	0.2796	4.829745	3.943749
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	0.036758	2.698893	3.933115	2.643920	1.498518	1.559600	1.689199	3.258602	0.7381	0.7976	0.3864	2.126876	1.936667
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	0.418137	2.460726	3.306207	3.076672	1.986714	1.556466	1.541763	2.573990	0.7617	0.8092	0.3248	1.924590	1.791223
2459488	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.667674	1.555357	0.484533	0.025161	0.219299	0.364766	11.110260	6.215369	0.7591	0.8112	0.3690	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 15 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 15, Node 1:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 15: 2459625

Antenna 15: 2459624

Antenna 15: 2459623

Antenna 15: 2459622

Antenna 15: 2459621

Antenna 15: 2459620

Antenna 15: 2459619

Antenna 15: 2459618

Antenna 15: 2459617

Antenna 15: 2459616

Antenna 15: 2459615

Antenna 15: 2459614

Antenna 15: 2459613

Antenna 15: 2459612

Antenna 15: 2459611

Antenna 15: 2459610

Antenna 15: 2459609

Antenna 15: 2459608

Antenna 15: 2459607

Antenna 15: 2459605

Antenna 15: 2459604

Antenna 15: 2459603

Antenna 15: 2459602

Antenna 15: 2459598

Antenna 15: 2459597

Antenna 15: 2459596

Antenna 15: 2459595

Antenna 15: 2459594

Antenna 15: 2459593

Antenna 15: 2459592

Antenna 15: 2459591

Antenna 15: 2459590

Antenna 15: 2459589

Antenna 15: 2459588

Antenna 15: 2459587

Antenna 15: 2459586

Antenna 15: 2459585

Antenna 15: 2459584

Antenna 15: 2459583

Antenna 15: 2459582

Antenna 15: 2459581

Antenna 15: 2459580

Antenna 15: 2459579

Antenna 15: 2459578

Antenna 15: 2459577

Antenna 15: 2459576

Antenna 15: 2459575

Antenna 15: 2459574

Antenna 15: 2459573

Antenna 15: 2459572

Antenna 15: 2459571

Antenna 15: 2459570

Antenna 15: 2459569

Antenna 15: 2459566

Antenna 15: 2459565

Antenna 15: 2459564

Antenna 15: 2459563

Antenna 15: 2459562

Antenna 15: 2459561

Antenna 15: 2459560

Antenna 15: 2459559

Antenna 15: 2459558

Antenna 15: 2459556

Antenna 15: 2459552

Antenna 15: 2459551

Antenna 15: 2459549

Antenna 15: 2459542

Antenna 15: 2459541

Antenna 15: 2459540

Antenna 15: 2459536

Antenna 15: 2459535

Antenna 15: 2459534

Antenna 15: 2459533

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶